Digital map users are often unaware of positional inaccuracy in underlying map data because the information is not visualized for the user. When a map contains positional inaccuracy, the geographical coordinates of a given map feature can differ from the coordinates of that feature on the actual surface of the earth. For example, a digital map can show inaccurate shoreline locations, which can cause issues when performing ship navigation. A plotted course that passes near land on the map may pass over land in reality. In this example, a ship navigator traveling on such a course would have to notice the problem and reroute. A potential solution would be to provide the planner with a visual indication of where uncertainty lies, which could allow the planner to plan a safer route using the original map.
Digital maps can include various types of data such as, but not limited to, vector data (e.g., points, lines, areas, etc.) that include geographic coordinates representing features on the earth, imagery obtained by satellite or aircraft, and gridded data like bathymetry, which are underwater depth measurements at distance intervals. Datasets can specify accuracy requirements, which can be used to determine uncertainty. For example, a shoreline vector dataset can specify that 90% of all identifiable shoreline features be located within 500 meters circular error of their true geographic position, which is an example of positional uncertainty. In another example, imagery can specify that it should be within 250 meters of the actual geographic position at sea level, which is another example of positional uncertainty. In yet another example, a bathymetry grid can specify a mathematical formula for the depth with a 95% confidence level, which is an example of depth uncertainty.
Visualization is the process by which vector data or other data are rendered on a display. Uncertainty in the data can also be visualized; however, if too is visualized on a map simultaneously, clutter can prevent a user from locating critical information on the map. Further, users may interpret the visualization of uncertainty in unexpected ways that may mislead them.